Overview

Dataset statistics

Number of variables29
Number of observations10000
Missing cells55825
Missing cells (%)19.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 MiB
Average record size in memory232.0 B

Variable types

Numeric17
Categorical6
DateTime1
Unsupported4
Text1

Alerts

id is highly overall correlated with total_pymnt and 2 other fieldsHigh correlation
loan_amnt is highly overall correlated with funded_amnt and 4 other fieldsHigh correlation
funded_amnt is highly overall correlated with loan_amnt and 4 other fieldsHigh correlation
int_rate is highly overall correlated with int_rate3High correlation
installment is highly overall correlated with loan_amnt and 4 other fieldsHigh correlation
delinq_2yrs is highly overall correlated with mths_since_last_delinqHigh correlation
mths_since_last_delinq is highly overall correlated with delinq_2yrsHigh correlation
open_acc is highly overall correlated with total_accHigh correlation
total_acc is highly overall correlated with open_accHigh correlation
out_prncp is highly overall correlated with loan_amnt and 3 other fieldsHigh correlation
total_pymnt is highly overall correlated with id and 5 other fieldsHigh correlation
total_rec_prncp is highly overall correlated with id and 2 other fieldsHigh correlation
total_rec_int is highly overall correlated with id and 5 other fieldsHigh correlation
int_rate3 is highly overall correlated with int_rateHigh correlation
term is highly overall correlated with out_prncpHigh correlation
loan_status is highly imbalanced (70.8%)Imbalance
term has 476 (4.8%) missing valuesMissing
int_rate has 476 (4.8%) missing valuesMissing
installment has 476 (4.8%) missing valuesMissing
emp_length has 881 (8.8%) missing valuesMissing
home_ownership has 476 (4.8%) missing valuesMissing
annual_inc has 476 (4.8%) missing valuesMissing
loan_status has 476 (4.8%) missing valuesMissing
purpose has 476 (4.8%) missing valuesMissing
dti has 476 (4.8%) missing valuesMissing
delinq_2yrs has 476 (4.8%) missing valuesMissing
earliest_cr_line has 476 (4.8%) missing valuesMissing
mths_since_last_delinq has 5900 (59.0%) missing valuesMissing
open_acc has 476 (4.8%) missing valuesMissing
revol_bal has 476 (4.8%) missing valuesMissing
total_acc has 476 (4.8%) missing valuesMissing
out_prncp has 476 (4.8%) missing valuesMissing
total_pymnt has 476 (4.8%) missing valuesMissing
total_rec_prncp has 476 (4.8%) missing valuesMissing
total_rec_int has 476 (4.8%) missing valuesMissing
wtd_loans has 10000 (100.0%) missing valuesMissing
interest_rate has 10000 (100.0%) missing valuesMissing
int_rate2 has 476 (4.8%) missing valuesMissing
num_rate has 10000 (100.0%) missing valuesMissing
numrate has 10000 (100.0%) missing valuesMissing
int_rate3 has 476 (4.8%) missing valuesMissing
id has unique valuesUnique
wtd_loans is an unsupported type, check if it needs cleaning or further analysisUnsupported
interest_rate is an unsupported type, check if it needs cleaning or further analysisUnsupported
num_rate is an unsupported type, check if it needs cleaning or further analysisUnsupported
numrate is an unsupported type, check if it needs cleaning or further analysisUnsupported
delinq_2yrs has 8025 (80.2%) zerosZeros
out_prncp has 1169 (11.7%) zerosZeros

Reproduction

Analysis started2023-11-10 19:31:33.979559
Analysis finished2023-11-10 19:32:31.388670
Duration57.41 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

id
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5143647.9
Minimum571203
Maximum10125066
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-11-10T14:32:31.539265image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum571203
5-th percentile1223026.2
Q12300882.5
median5605038.5
Q37435741
95-th percentile9705338.4
Maximum10125066
Range9553863
Interquartile range (IQR)5134858.5

Descriptive statistics

Standard deviation2827943.8
Coefficient of variation (CV)0.54979344
Kurtosis-1.3032773
Mean5143647.9
Median Absolute Deviation (MAD)2418180
Skewness0.034461233
Sum5.1436479 × 1010
Variance7.9972663 × 1012
MonotonicityNot monotonic
2023-11-10T14:32:31.776208image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
571203 1
 
< 0.1%
6888279 1
 
< 0.1%
6884941 1
 
< 0.1%
6885382 1
 
< 0.1%
6885826 1
 
< 0.1%
6886319 1
 
< 0.1%
6886848 1
 
< 0.1%
6887364 1
 
< 0.1%
6887824 1
 
< 0.1%
6888765 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
571203 1
< 0.1%
641849 1
< 0.1%
694891 1
< 0.1%
734736 1
< 0.1%
784712 1
< 0.1%
807342 1
< 0.1%
843448 1
< 0.1%
880114 1
< 0.1%
974654 1
< 0.1%
999547 1
< 0.1%
ValueCountFrequency (%)
10125066 1
< 0.1%
10124808 1
< 0.1%
10123803 1
< 0.1%
10123620 1
< 0.1%
10123424 1
< 0.1%
10123100 1
< 0.1%
10122896 1
< 0.1%
10122772 1
< 0.1%
10122507 1
< 0.1%
10122303 1
< 0.1%

loan_amnt
Real number (ℝ)

HIGH CORRELATION 

Distinct697
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14054.808
Minimum1000
Maximum35000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-11-10T14:32:32.017230image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile3300
Q18000
median12000
Q319400
95-th percentile30000
Maximum35000
Range34000
Interquartile range (IQR)11400

Descriptive statistics

Standard deviation8108.6587
Coefficient of variation (CV)0.57693133
Kurtosis-0.034504762
Mean14054.808
Median Absolute Deviation (MAD)5475
Skewness0.75167769
Sum1.4054808 × 108
Variance65750346
MonotonicityNot monotonic
2023-11-10T14:32:32.287676image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 720
 
7.2%
15000 559
 
5.6%
12000 546
 
5.5%
20000 447
 
4.5%
8000 334
 
3.3%
6000 327
 
3.3%
35000 304
 
3.0%
16000 269
 
2.7%
18000 264
 
2.6%
5000 257
 
2.6%
Other values (687) 5973
59.7%
ValueCountFrequency (%)
1000 30
0.3%
1100 2
 
< 0.1%
1150 1
 
< 0.1%
1200 22
0.2%
1225 1
 
< 0.1%
1325 1
 
< 0.1%
1350 1
 
< 0.1%
1400 8
 
0.1%
1450 4
 
< 0.1%
1500 28
0.3%
ValueCountFrequency (%)
35000 304
3.0%
34975 1
 
< 0.1%
34800 1
 
< 0.1%
34500 1
 
< 0.1%
34475 3
 
< 0.1%
34350 1
 
< 0.1%
34000 9
 
0.1%
33950 10
 
0.1%
33600 4
 
< 0.1%
33425 12
 
0.1%

funded_amnt
Real number (ℝ)

HIGH CORRELATION 

Distinct698
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14052.73
Minimum1000
Maximum35000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-11-10T14:32:32.600915image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile3300
Q18000
median12000
Q319400
95-th percentile30000
Maximum35000
Range34000
Interquartile range (IQR)11400

Descriptive statistics

Standard deviation8107.6932
Coefficient of variation (CV)0.57694791
Kurtosis-0.032890733
Mean14052.73
Median Absolute Deviation (MAD)5462.5
Skewness0.75225334
Sum1.405273 × 108
Variance65734690
MonotonicityNot monotonic
2023-11-10T14:32:32.859410image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 720
 
7.2%
15000 559
 
5.6%
12000 545
 
5.5%
20000 447
 
4.5%
8000 334
 
3.3%
6000 327
 
3.3%
35000 304
 
3.0%
16000 269
 
2.7%
18000 265
 
2.6%
5000 257
 
2.6%
Other values (688) 5973
59.7%
ValueCountFrequency (%)
1000 30
0.3%
1100 2
 
< 0.1%
1150 1
 
< 0.1%
1200 22
0.2%
1225 1
 
< 0.1%
1325 1
 
< 0.1%
1350 1
 
< 0.1%
1400 8
 
0.1%
1450 4
 
< 0.1%
1500 28
0.3%
ValueCountFrequency (%)
35000 304
3.0%
34975 1
 
< 0.1%
34800 1
 
< 0.1%
34500 1
 
< 0.1%
34475 3
 
< 0.1%
34350 1
 
< 0.1%
34000 9
 
0.1%
33950 10
 
0.1%
33600 4
 
< 0.1%
33425 12
 
0.1%

term
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing476
Missing (%)4.8%
Memory size78.2 KiB
36 months
7269 
60 months
2255 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters95240
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row 60 months
2nd row 36 months
3rd row 60 months
4th row 36 months
5th row 36 months

Common Values

ValueCountFrequency (%)
36 months 7269
72.7%
60 months 2255
 
22.6%
(Missing) 476
 
4.8%

Length

2023-11-10T14:32:33.261669image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-10T14:32:33.432983image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
months 9524
50.0%
36 7269
38.2%
60 2255
 
11.8%

Most occurring characters

ValueCountFrequency (%)
19048
20.0%
6 9524
10.0%
m 9524
10.0%
o 9524
10.0%
n 9524
10.0%
t 9524
10.0%
h 9524
10.0%
s 9524
10.0%
3 7269
 
7.6%
0 2255
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 57144
60.0%
Space Separator 19048
 
20.0%
Decimal Number 19048
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
m 9524
16.7%
o 9524
16.7%
n 9524
16.7%
t 9524
16.7%
h 9524
16.7%
s 9524
16.7%
Decimal Number
ValueCountFrequency (%)
6 9524
50.0%
3 7269
38.2%
0 2255
 
11.8%
Space Separator
ValueCountFrequency (%)
19048
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 57144
60.0%
Common 38096
40.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
m 9524
16.7%
o 9524
16.7%
n 9524
16.7%
t 9524
16.7%
h 9524
16.7%
s 9524
16.7%
Common
ValueCountFrequency (%)
19048
50.0%
6 9524
25.0%
3 7269
 
19.1%
0 2255
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 95240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
19048
20.0%
6 9524
10.0%
m 9524
10.0%
o 9524
10.0%
n 9524
10.0%
t 9524
10.0%
h 9524
10.0%
s 9524
10.0%
3 7269
 
7.6%
0 2255
 
2.4%

int_rate
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct134
Distinct (%)1.4%
Missing476
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean14.277852
Minimum6.03
Maximum26.06
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-11-10T14:32:33.626817image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum6.03
5-th percentile7.62
Q111.14
median14.09
Q317.27
95-th percentile22.4
Maximum26.06
Range20.03
Interquartile range (IQR)6.13

Descriptive statistics

Standard deviation4.4301591
Coefficient of variation (CV)0.31028191
Kurtosis-0.46512934
Mean14.277852
Median Absolute Deviation (MAD)3.1
Skewness0.24772703
Sum135982.26
Variance19.62631
MonotonicityNot monotonic
2023-11-10T14:32:33.904008image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.12 485
 
4.9%
13.11 432
 
4.3%
8.9 357
 
3.6%
14.33 351
 
3.5%
7.9 321
 
3.2%
11.14 318
 
3.2%
15.31 285
 
2.9%
16.29 265
 
2.6%
7.62 262
 
2.6%
10.16 223
 
2.2%
Other values (124) 6225
62.3%
(Missing) 476
 
4.8%
ValueCountFrequency (%)
6.03 220
2.2%
6.62 184
1.8%
6.97 15
 
0.1%
7.51 12
 
0.1%
7.62 262
2.6%
7.9 321
3.2%
8.6 23
 
0.2%
8.9 357
3.6%
9.25 26
 
0.3%
9.67 75
 
0.8%
ValueCountFrequency (%)
26.06 6
 
0.1%
25.99 3
 
< 0.1%
25.89 8
0.1%
25.83 9
0.1%
25.8 9
0.1%
25.57 8
0.1%
25.28 5
 
0.1%
24.99 11
0.1%
24.89 15
0.1%
24.83 7
0.1%

installment
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct5174
Distinct (%)54.3%
Missing476
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean442.62661
Minimum30.44
Maximum1388.45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-11-10T14:32:34.233940image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum30.44
5-th percentile118.12
Q1266.575
median398.51
Q3576.7375
95-th percentile920.5635
Maximum1388.45
Range1358.01
Interquartile range (IQR)310.1625

Descriptive statistics

Standard deviation244.52212
Coefficient of variation (CV)0.55243429
Kurtosis0.83526776
Mean442.62661
Median Absolute Deviation (MAD)149.515
Skewness0.93200479
Sum4215575.8
Variance59791.065
MonotonicityNot monotonic
2023-11-10T14:32:34.584981image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
332.72 40
 
0.4%
399.26 36
 
0.4%
499.08 34
 
0.3%
337.47 34
 
0.3%
665.44 29
 
0.3%
328.06 29
 
0.3%
404.97 25
 
0.2%
343.39 25
 
0.2%
412.06 25
 
0.2%
635.07 23
 
0.2%
Other values (5164) 9224
92.2%
(Missing) 476
 
4.8%
ValueCountFrequency (%)
30.44 1
< 0.1%
31.17 1
< 0.1%
31.3 1
< 0.1%
32 1
< 0.1%
32.14 1
< 0.1%
32.35 1
< 0.1%
32.42 1
< 0.1%
32.81 1
< 0.1%
33.42 1
< 0.1%
33.75 1
< 0.1%
ValueCountFrequency (%)
1388.45 1
< 0.1%
1366.36 1
< 0.1%
1363.98 1
< 0.1%
1359.96 1
< 0.1%
1353.93 2
< 0.1%
1349.38 1
< 0.1%
1336.31 1
< 0.1%
1331.25 1
< 0.1%
1327.45 1
< 0.1%
1318.63 1
< 0.1%

emp_length
Categorical

MISSING 

Distinct11
Distinct (%)0.1%
Missing881
Missing (%)8.8%
Memory size78.2 KiB
10+ years
3054 
2 years
869 
5 years
753 
3 years
692 
< 1 year
657 
Other values (6)
3094 

Length

Max length9
Median length7
Mean length7.6779252
Min length6

Characters and Unicode

Total characters70015
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10+ years
2nd row10+ years
3rd row2 years
4th row3 years
5th row2 years

Common Values

ValueCountFrequency (%)
10+ years 3054
30.5%
2 years 869
 
8.7%
5 years 753
 
7.5%
3 years 692
 
6.9%
< 1 year 657
 
6.6%
6 years 618
 
6.2%
1 year 583
 
5.8%
7 years 558
 
5.6%
4 years 537
 
5.4%
8 years 449
 
4.5%
(Missing) 881
 
8.8%

Length

2023-11-10T14:32:34.934605image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
years 7879
41.7%
10 3054
 
16.2%
1 1240
 
6.6%
year 1240
 
6.6%
2 869
 
4.6%
5 753
 
4.0%
3 692
 
3.7%
657
 
3.5%
6 618
 
3.3%
7 558
 
3.0%
Other values (3) 1335
 
7.1%

Most occurring characters

ValueCountFrequency (%)
9776
14.0%
y 9119
13.0%
e 9119
13.0%
a 9119
13.0%
r 9119
13.0%
s 7879
11.3%
1 4294
6.1%
0 3054
 
4.4%
+ 3054
 
4.4%
2 869
 
1.2%
Other values (8) 4613
6.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 44355
63.4%
Decimal Number 12173
 
17.4%
Space Separator 9776
 
14.0%
Math Symbol 3711
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4294
35.3%
0 3054
25.1%
2 869
 
7.1%
5 753
 
6.2%
3 692
 
5.7%
6 618
 
5.1%
7 558
 
4.6%
4 537
 
4.4%
8 449
 
3.7%
9 349
 
2.9%
Lowercase Letter
ValueCountFrequency (%)
y 9119
20.6%
e 9119
20.6%
a 9119
20.6%
r 9119
20.6%
s 7879
17.8%
Math Symbol
ValueCountFrequency (%)
+ 3054
82.3%
< 657
 
17.7%
Space Separator
ValueCountFrequency (%)
9776
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 44355
63.4%
Common 25660
36.6%

Most frequent character per script

Common
ValueCountFrequency (%)
9776
38.1%
1 4294
16.7%
0 3054
 
11.9%
+ 3054
 
11.9%
2 869
 
3.4%
5 753
 
2.9%
3 692
 
2.7%
< 657
 
2.6%
6 618
 
2.4%
7 558
 
2.2%
Other values (3) 1335
 
5.2%
Latin
ValueCountFrequency (%)
y 9119
20.6%
e 9119
20.6%
a 9119
20.6%
r 9119
20.6%
s 7879
17.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70015
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9776
14.0%
y 9119
13.0%
e 9119
13.0%
a 9119
13.0%
r 9119
13.0%
s 7879
11.3%
1 4294
6.1%
0 3054
 
4.4%
+ 3054
 
4.4%
2 869
 
1.2%
Other values (8) 4613
6.6%

home_ownership
Categorical

MISSING 

Distinct5
Distinct (%)0.1%
Missing476
Missing (%)4.8%
Memory size78.2 KiB
MORTGAGE
4839 
RENT
3855 
OWN
828 
OTHER
 
1
NONE
 
1

Length

Max length8
Median length8
Mean length5.9455061
Min length3

Characters and Unicode

Total characters56625
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowMORTGAGE
2nd rowMORTGAGE
3rd rowMORTGAGE
4th rowRENT
5th rowRENT

Common Values

ValueCountFrequency (%)
MORTGAGE 4839
48.4%
RENT 3855
38.6%
OWN 828
 
8.3%
OTHER 1
 
< 0.1%
NONE 1
 
< 0.1%
(Missing) 476
 
4.8%

Length

2023-11-10T14:32:35.213343image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-10T14:32:35.418246image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
mortgage 4839
50.8%
rent 3855
40.5%
own 828
 
8.7%
other 1
 
< 0.1%
none 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
G 9678
17.1%
E 8696
15.4%
R 8695
15.4%
T 8695
15.4%
O 5669
10.0%
M 4839
8.5%
A 4839
8.5%
N 4685
8.3%
W 828
 
1.5%
H 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 56625
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G 9678
17.1%
E 8696
15.4%
R 8695
15.4%
T 8695
15.4%
O 5669
10.0%
M 4839
8.5%
A 4839
8.5%
N 4685
8.3%
W 828
 
1.5%
H 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 56625
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 9678
17.1%
E 8696
15.4%
R 8695
15.4%
T 8695
15.4%
O 5669
10.0%
M 4839
8.5%
A 4839
8.5%
N 4685
8.3%
W 828
 
1.5%
H 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 56625
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
G 9678
17.1%
E 8696
15.4%
R 8695
15.4%
T 8695
15.4%
O 5669
10.0%
M 4839
8.5%
A 4839
8.5%
N 4685
8.3%
W 828
 
1.5%
H 1
 
< 0.1%

annual_inc
Real number (ℝ)

MISSING 

Distinct1492
Distinct (%)15.7%
Missing476
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean71655.752
Minimum7500
Maximum1000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-11-10T14:32:35.671202image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum7500
5-th percentile29000
Q145000
median61000
Q386000
95-th percentile140850
Maximum1000000
Range992500
Interquartile range (IQR)41000

Descriptive statistics

Standard deviation45362.834
Coefficient of variation (CV)0.6330662
Kurtosis61.214623
Mean71655.752
Median Absolute Deviation (MAD)19000
Skewness5.0068795
Sum6.8244938 × 108
Variance2.0577868 × 109
MonotonicityNot monotonic
2023-11-10T14:32:35.993466image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50000 392
 
3.9%
60000 361
 
3.6%
40000 290
 
2.9%
65000 285
 
2.9%
70000 266
 
2.7%
55000 252
 
2.5%
45000 245
 
2.5%
75000 237
 
2.4%
80000 231
 
2.3%
35000 191
 
1.9%
Other values (1482) 6774
67.7%
(Missing) 476
 
4.8%
ValueCountFrequency (%)
7500 1
 
< 0.1%
8400 1
 
< 0.1%
8832 1
 
< 0.1%
10000 1
 
< 0.1%
10492.8 1
 
< 0.1%
11000 2
 
< 0.1%
11111 1
 
< 0.1%
11853 1
 
< 0.1%
12000 7
0.1%
12400 1
 
< 0.1%
ValueCountFrequency (%)
1000000 1
< 0.1%
900009 1
< 0.1%
897000 1
< 0.1%
760000 1
< 0.1%
600000 2
< 0.1%
550000 1
< 0.1%
525000 1
< 0.1%
500000 2
< 0.1%
450000 1
< 0.1%
444000 1
< 0.1%

loan_status
Categorical

IMBALANCE  MISSING 

Distinct7
Distinct (%)0.1%
Missing476
Missing (%)4.8%
Memory size78.2 KiB
Current
8122 
Fully Paid
951 
Charged Off
 
218
Late (31-120 days)
 
148
In Grace Period
 
48
Other values (2)
 
37

Length

Max length18
Median length7
Mean length7.6244225
Min length7

Characters and Unicode

Total characters72615
Distinct characters33
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCurrent
2nd rowCurrent
3rd rowLate (31-120 days)
4th rowFully Paid
5th rowCurrent

Common Values

ValueCountFrequency (%)
Current 8122
81.2%
Fully Paid 951
 
9.5%
Charged Off 218
 
2.2%
Late (31-120 days) 148
 
1.5%
In Grace Period 48
 
0.5%
Late (16-30 days) 21
 
0.2%
Default 16
 
0.2%
(Missing) 476
 
4.8%

Length

2023-11-10T14:32:36.318985image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-10T14:32:36.547143image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
current 8122
73.0%
fully 951
 
8.5%
paid 951
 
8.5%
charged 218
 
2.0%
off 218
 
2.0%
late 169
 
1.5%
days 169
 
1.5%
31-120 148
 
1.3%
in 48
 
0.4%
grace 48
 
0.4%
Other values (3) 85
 
0.8%

Most occurring characters

ValueCountFrequency (%)
r 16558
22.8%
u 9089
12.5%
e 8621
11.9%
C 8340
11.5%
t 8307
11.4%
n 8170
11.3%
l 1918
 
2.6%
1603
 
2.2%
a 1571
 
2.2%
d 1386
 
1.9%
Other values (23) 7052
9.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 58892
81.1%
Uppercase Letter 10789
 
14.9%
Space Separator 1603
 
2.2%
Decimal Number 824
 
1.1%
Open Punctuation 169
 
0.2%
Dash Punctuation 169
 
0.2%
Close Punctuation 169
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 16558
28.1%
u 9089
15.4%
e 8621
14.6%
t 8307
14.1%
n 8170
13.9%
l 1918
 
3.3%
a 1571
 
2.7%
d 1386
 
2.4%
y 1120
 
1.9%
i 999
 
1.7%
Other values (6) 1153
 
2.0%
Uppercase Letter
ValueCountFrequency (%)
C 8340
77.3%
P 999
 
9.3%
F 951
 
8.8%
O 218
 
2.0%
L 169
 
1.6%
I 48
 
0.4%
G 48
 
0.4%
D 16
 
0.1%
Decimal Number
ValueCountFrequency (%)
1 317
38.5%
3 169
20.5%
0 169
20.5%
2 148
18.0%
6 21
 
2.5%
Space Separator
ValueCountFrequency (%)
1603
100.0%
Open Punctuation
ValueCountFrequency (%)
( 169
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 169
100.0%
Close Punctuation
ValueCountFrequency (%)
) 169
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 69681
96.0%
Common 2934
 
4.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 16558
23.8%
u 9089
13.0%
e 8621
12.4%
C 8340
12.0%
t 8307
11.9%
n 8170
11.7%
l 1918
 
2.8%
a 1571
 
2.3%
d 1386
 
2.0%
y 1120
 
1.6%
Other values (14) 4601
 
6.6%
Common
ValueCountFrequency (%)
1603
54.6%
1 317
 
10.8%
( 169
 
5.8%
3 169
 
5.8%
- 169
 
5.8%
0 169
 
5.8%
) 169
 
5.8%
2 148
 
5.0%
6 21
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72615
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 16558
22.8%
u 9089
12.5%
e 8621
11.9%
C 8340
11.5%
t 8307
11.4%
n 8170
11.3%
l 1918
 
2.6%
1603
 
2.2%
a 1571
 
2.2%
d 1386
 
1.9%
Other values (23) 7052
9.7%

purpose
Categorical

MISSING 

Distinct13
Distinct (%)0.1%
Missing476
Missing (%)4.8%
Memory size78.2 KiB
debt_consolidation
5665 
credit_card
2214 
home_improvement
 
497
other
 
431
major_purchase
 
189
Other values (8)
 
528

Length

Max length18
Median length18
Mean length15.064679
Min length3

Characters and Unicode

Total characters143476
Distinct characters22
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcredit_card
2nd rowsmall_business
3rd rowsmall_business
4th rowdebt_consolidation
5th rowdebt_consolidation

Common Values

ValueCountFrequency (%)
debt_consolidation 5665
56.6%
credit_card 2214
 
22.1%
home_improvement 497
 
5.0%
other 431
 
4.3%
major_purchase 189
 
1.9%
small_business 147
 
1.5%
car 81
 
0.8%
medical 72
 
0.7%
wedding 61
 
0.6%
house 55
 
0.5%
Other values (3) 112
 
1.1%
(Missing) 476
 
4.8%

Length

2023-11-10T14:32:36.806920image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
debt_consolidation 5665
59.5%
credit_card 2214
 
23.2%
home_improvement 497
 
5.2%
other 431
 
4.5%
major_purchase 189
 
2.0%
small_business 147
 
1.5%
car 81
 
0.9%
medical 72
 
0.8%
wedding 61
 
0.6%
house 55
 
0.6%
Other values (3) 112
 
1.2%

Most occurring characters

ValueCountFrequency (%)
o 18764
13.1%
d 15952
11.1%
t 14522
10.1%
i 14421
10.1%
n 12159
8.5%
c 10485
7.3%
e 10385
7.2%
_ 8724
 
6.1%
a 8669
 
6.0%
s 6497
 
4.5%
Other values (12) 22898
16.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 134752
93.9%
Connector Punctuation 8724
 
6.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 18764
13.9%
d 15952
11.8%
t 14522
10.8%
i 14421
10.7%
n 12159
9.0%
c 10485
7.8%
e 10385
7.7%
a 8669
6.4%
s 6497
 
4.8%
l 6043
 
4.5%
Other values (11) 16855
12.5%
Connector Punctuation
ValueCountFrequency (%)
_ 8724
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 134752
93.9%
Common 8724
 
6.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 18764
13.9%
d 15952
11.8%
t 14522
10.8%
i 14421
10.7%
n 12159
9.0%
c 10485
7.8%
e 10385
7.7%
a 8669
6.4%
s 6497
 
4.8%
l 6043
 
4.5%
Other values (11) 16855
12.5%
Common
ValueCountFrequency (%)
_ 8724
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 143476
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 18764
13.1%
d 15952
11.1%
t 14522
10.1%
i 14421
10.1%
n 12159
8.5%
c 10485
7.3%
e 10385
7.2%
_ 8724
 
6.1%
a 8669
 
6.0%
s 6497
 
4.5%
Other values (12) 22898
16.0%

addr_state
Categorical

Distinct45
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
CA
1685 
NY
868 
TX
783 
FL
648 
NJ
 
400
Other values (40)
5616 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters20000
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMA
2nd rowWA
3rd rowNY
4th rowNJ
5th rowVA

Common Values

ValueCountFrequency (%)
CA 1685
16.9%
NY 868
 
8.7%
TX 783
 
7.8%
FL 648
 
6.5%
NJ 400
 
4.0%
PA 367
 
3.7%
IL 362
 
3.6%
VA 317
 
3.2%
GA 310
 
3.1%
NC 287
 
2.9%
Other values (35) 3973
39.7%

Length

2023-11-10T14:32:37.023923image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ca 1685
16.9%
ny 868
 
8.7%
tx 783
 
7.8%
fl 648
 
6.5%
nj 400
 
4.0%
pa 367
 
3.7%
il 362
 
3.6%
va 317
 
3.2%
ga 310
 
3.1%
nc 287
 
2.9%
Other values (35) 3973
39.7%

Most occurring characters

ValueCountFrequency (%)
A 3685
18.4%
C 2474
12.4%
N 2211
11.1%
L 1257
 
6.3%
T 1187
 
5.9%
M 1013
 
5.1%
Y 1009
 
5.0%
I 954
 
4.8%
O 841
 
4.2%
X 783
 
3.9%
Other values (14) 4586
22.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 20000
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 3685
18.4%
C 2474
12.4%
N 2211
11.1%
L 1257
 
6.3%
T 1187
 
5.9%
M 1013
 
5.1%
Y 1009
 
5.0%
I 954
 
4.8%
O 841
 
4.2%
X 783
 
3.9%
Other values (14) 4586
22.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 20000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 3685
18.4%
C 2474
12.4%
N 2211
11.1%
L 1257
 
6.3%
T 1187
 
5.9%
M 1013
 
5.1%
Y 1009
 
5.0%
I 954
 
4.8%
O 841
 
4.2%
X 783
 
3.9%
Other values (14) 4586
22.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 3685
18.4%
C 2474
12.4%
N 2211
11.1%
L 1257
 
6.3%
T 1187
 
5.9%
M 1013
 
5.1%
Y 1009
 
5.0%
I 954
 
4.8%
O 841
 
4.2%
X 783
 
3.9%
Other values (14) 4586
22.9%

dti
Real number (ℝ)

MISSING 

Distinct2955
Distinct (%)31.0%
Missing476
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean17.146927
Minimum0
Maximum34.98
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-11-10T14:32:37.237067image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.1
Q111.52
median16.84
Q322.59
95-th percentile30.0885
Maximum34.98
Range34.98
Interquartile range (IQR)11.07

Descriptive statistics

Standard deviation7.5916009
Coefficient of variation (CV)0.44273828
Kurtosis-0.64702699
Mean17.146927
Median Absolute Deviation (MAD)5.53
Skewness0.13199079
Sum163307.33
Variance57.632404
MonotonicityNot monotonic
2023-11-10T14:32:37.500150image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.37 12
 
0.1%
14.4 11
 
0.1%
17.58 11
 
0.1%
16.29 11
 
0.1%
12.63 11
 
0.1%
14.74 11
 
0.1%
17.32 11
 
0.1%
13.14 10
 
0.1%
11.6 10
 
0.1%
11.35 10
 
0.1%
Other values (2945) 9416
94.2%
(Missing) 476
 
4.8%
ValueCountFrequency (%)
0 4
< 0.1%
0.01 1
 
< 0.1%
0.13 1
 
< 0.1%
0.15 1
 
< 0.1%
0.2 1
 
< 0.1%
0.25 3
< 0.1%
0.26 1
 
< 0.1%
0.41 1
 
< 0.1%
0.45 2
< 0.1%
0.47 1
 
< 0.1%
ValueCountFrequency (%)
34.98 2
< 0.1%
34.97 1
 
< 0.1%
34.96 4
< 0.1%
34.95 1
 
< 0.1%
34.9 2
< 0.1%
34.88 1
 
< 0.1%
34.86 1
 
< 0.1%
34.85 1
 
< 0.1%
34.84 1
 
< 0.1%
34.8 1
 
< 0.1%

delinq_2yrs
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct11
Distinct (%)0.1%
Missing476
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean0.23876522
Minimum0
Maximum11
Zeros8025
Zeros (%)80.2%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-11-10T14:32:37.678416image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum11
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.69145547
Coefficient of variation (CV)2.8959639
Kurtosis39.205653
Mean0.23876522
Median Absolute Deviation (MAD)0
Skewness4.9444618
Sum2274
Variance0.47811067
MonotonicityNot monotonic
2023-11-10T14:32:37.831415image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 8025
80.2%
1 1027
 
10.3%
2 316
 
3.2%
3 90
 
0.9%
4 33
 
0.3%
5 12
 
0.1%
6 10
 
0.1%
8 4
 
< 0.1%
7 3
 
< 0.1%
9 2
 
< 0.1%
(Missing) 476
 
4.8%
ValueCountFrequency (%)
0 8025
80.2%
1 1027
 
10.3%
2 316
 
3.2%
3 90
 
0.9%
4 33
 
0.3%
5 12
 
0.1%
6 10
 
0.1%
7 3
 
< 0.1%
8 4
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
11 2
 
< 0.1%
9 2
 
< 0.1%
8 4
 
< 0.1%
7 3
 
< 0.1%
6 10
 
0.1%
5 12
 
0.1%
4 33
 
0.3%
3 90
 
0.9%
2 316
 
3.2%
1 1027
10.3%

earliest_cr_line
Date

MISSING 

Distinct9397
Distinct (%)98.7%
Missing476
Missing (%)4.8%
Memory size78.2 KiB
Minimum1970-01-12 12:47:00
Maximum2069-12-27 12:00:00
2023-11-10T14:32:38.013173image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:32:38.229382image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

mths_since_last_delinq
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct87
Distinct (%)2.1%
Missing5900
Missing (%)59.0%
Infinite0
Infinite (%)0.0%
Mean34.988537
Minimum0
Maximum122
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-11-10T14:32:38.454086image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q117
median32
Q349
95-th percentile75
Maximum122
Range122
Interquartile range (IQR)32

Descriptive statistics

Standard deviation21.474509
Coefficient of variation (CV)0.61375842
Kurtosis-0.76340332
Mean34.988537
Median Absolute Deviation (MAD)16
Skewness0.4635379
Sum143453
Variance461.15454
MonotonicityNot monotonic
2023-11-10T14:32:38.700309image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 93
 
0.9%
8 89
 
0.9%
17 84
 
0.8%
14 84
 
0.8%
10 84
 
0.8%
12 82
 
0.8%
36 81
 
0.8%
15 80
 
0.8%
11 79
 
0.8%
7 78
 
0.8%
Other values (77) 3266
32.7%
(Missing) 5900
59.0%
ValueCountFrequency (%)
0 5
 
0.1%
1 26
 
0.3%
2 27
 
0.3%
3 21
 
0.2%
4 32
 
0.3%
5 42
0.4%
6 50
0.5%
7 78
0.8%
8 89
0.9%
9 76
0.8%
ValueCountFrequency (%)
122 1
 
< 0.1%
96 1
 
< 0.1%
86 1
 
< 0.1%
83 4
 
< 0.1%
82 7
 
0.1%
81 32
0.3%
80 37
0.4%
79 23
0.2%
78 25
0.2%
77 25
0.2%

open_acc
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct36
Distinct (%)0.4%
Missing476
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean11.043784
Minimum1
Maximum39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-11-10T14:32:38.949677image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q18
median10
Q314
95-th percentile20
Maximum39
Range38
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.561028
Coefficient of variation (CV)0.41299503
Kurtosis1.3156019
Mean11.043784
Median Absolute Deviation (MAD)3
Skewness0.93522306
Sum105181
Variance20.802976
MonotonicityNot monotonic
2023-11-10T14:32:39.192239image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
9 948
9.5%
10 902
9.0%
8 892
8.9%
11 847
 
8.5%
7 801
 
8.0%
12 798
 
8.0%
6 628
 
6.3%
13 600
 
6.0%
14 488
 
4.9%
15 439
 
4.4%
Other values (26) 2181
21.8%
(Missing) 476
 
4.8%
ValueCountFrequency (%)
1 2
 
< 0.1%
2 29
 
0.3%
3 81
 
0.8%
4 202
 
2.0%
5 404
4.0%
6 628
6.3%
7 801
8.0%
8 892
8.9%
9 948
9.5%
10 902
9.0%
ValueCountFrequency (%)
39 1
 
< 0.1%
38 1
 
< 0.1%
36 1
 
< 0.1%
34 1
 
< 0.1%
32 3
 
< 0.1%
31 6
 
0.1%
30 3
 
< 0.1%
29 4
 
< 0.1%
28 9
0.1%
27 16
0.2%

revol_bal
Real number (ℝ)

MISSING 

Distinct8180
Distinct (%)85.9%
Missing476
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean15982.998
Minimum0
Maximum376679
Zeros25
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-11-10T14:32:39.446266image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2646.15
Q17151
median12495
Q320596
95-th percentile38079.85
Maximum376679
Range376679
Interquartile range (IQR)13445

Descriptive statistics

Standard deviation15177.648
Coefficient of variation (CV)0.94961208
Kurtosis72.197975
Mean15982.998
Median Absolute Deviation (MAD)6204
Skewness5.6083162
Sum1.5222208 × 108
Variance2.30361 × 108
MonotonicityNot monotonic
2023-11-10T14:32:39.710136image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 25
 
0.2%
7201 5
 
0.1%
15883 4
 
< 0.1%
14631 4
 
< 0.1%
5410 4
 
< 0.1%
7719 4
 
< 0.1%
13651 4
 
< 0.1%
6150 4
 
< 0.1%
11446 4
 
< 0.1%
5220 4
 
< 0.1%
Other values (8170) 9462
94.6%
(Missing) 476
 
4.8%
ValueCountFrequency (%)
0 25
0.2%
1 1
 
< 0.1%
7 1
 
< 0.1%
9 2
 
< 0.1%
10 1
 
< 0.1%
12 1
 
< 0.1%
15 2
 
< 0.1%
19 1
 
< 0.1%
31 1
 
< 0.1%
34 1
 
< 0.1%
ValueCountFrequency (%)
376679 1
< 0.1%
262741 1
< 0.1%
245619 1
< 0.1%
225925 1
< 0.1%
212032 1
< 0.1%
201757 1
< 0.1%
199713 1
< 0.1%
195540 1
< 0.1%
186291 1
< 0.1%
178858 1
< 0.1%

total_acc
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct64
Distinct (%)0.7%
Missing476
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean24.51764
Minimum3
Maximum68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-11-10T14:32:40.146517image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile9
Q117
median23
Q331
95-th percentile45
Maximum68
Range65
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.887693
Coefficient of variation (CV)0.44407589
Kurtosis0.43208523
Mean24.51764
Median Absolute Deviation (MAD)7
Skewness0.7221529
Sum233506
Variance118.54185
MonotonicityNot monotonic
2023-11-10T14:32:40.396391image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 402
 
4.0%
19 380
 
3.8%
21 376
 
3.8%
18 373
 
3.7%
22 351
 
3.5%
25 341
 
3.4%
16 337
 
3.4%
23 337
 
3.4%
17 332
 
3.3%
15 332
 
3.3%
Other values (54) 5963
59.6%
(Missing) 476
 
4.8%
ValueCountFrequency (%)
3 13
 
0.1%
4 18
 
0.2%
5 37
 
0.4%
6 72
 
0.7%
7 95
 
0.9%
8 109
1.1%
9 159
1.6%
10 191
1.9%
11 209
2.1%
12 246
2.5%
ValueCountFrequency (%)
68 1
 
< 0.1%
67 1
 
< 0.1%
65 1
 
< 0.1%
63 24
0.2%
62 8
 
0.1%
61 6
 
0.1%
60 11
0.1%
59 10
0.1%
58 6
 
0.1%
57 16
0.2%

out_prncp
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct8224
Distinct (%)86.4%
Missing476
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean10253.674
Minimum0
Maximum34413.52
Zeros1169
Zeros (%)11.7%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-11-10T14:32:40.618917image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14273.3875
median8745.425
Q315055.438
95-th percentile25997.232
Maximum34413.52
Range34413.52
Interquartile range (IQR)10782.05

Descriptive statistics

Standard deviation7963.3
Coefficient of variation (CV)0.77662893
Kurtosis0.10410446
Mean10253.674
Median Absolute Deviation (MAD)5238.94
Skewness0.78593003
Sum97655993
Variance63414148
MonotonicityNot monotonic
2023-11-10T14:32:40.866009image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1169
 
11.7%
9296.47 4
 
< 0.1%
11155.76 4
 
< 0.1%
9533.31 3
 
< 0.1%
14289.49 3
 
< 0.1%
7155.53 3
 
< 0.1%
11104.66 3
 
< 0.1%
32542.4 3
 
< 0.1%
11176.13 3
 
< 0.1%
18140.92 3
 
< 0.1%
Other values (8214) 8326
83.3%
(Missing) 476
 
4.8%
ValueCountFrequency (%)
0 1169
11.7%
92.92 1
 
< 0.1%
197.25 1
 
< 0.1%
197.48 1
 
< 0.1%
231.47 1
 
< 0.1%
322.5 1
 
< 0.1%
348.14 1
 
< 0.1%
391.67 1
 
< 0.1%
446.86 1
 
< 0.1%
495.27 1
 
< 0.1%
ValueCountFrequency (%)
34413.52 1
< 0.1%
34411.69 1
< 0.1%
34381.72 1
< 0.1%
34374.93 1
< 0.1%
34334.69 1
< 0.1%
34326.1 1
< 0.1%
34318.96 1
< 0.1%
34315.92 1
< 0.1%
34306.82 1
< 0.1%
34291.79 1
< 0.1%

total_pymnt
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9282
Distinct (%)97.5%
Missing476
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean5225.2409
Minimum34.14
Maximum44231.08
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-11-10T14:32:41.100589image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum34.14
5-th percentile566.269
Q11676.3125
median3500.04
Q36736.965
95-th percentile16303.613
Maximum44231.08
Range44196.94
Interquartile range (IQR)5060.6525

Descriptive statistics

Standard deviation5499.4787
Coefficient of variation (CV)1.0524833
Kurtosis8.8775758
Mean5225.2409
Median Absolute Deviation (MAD)2179.975
Skewness2.552875
Sum49765195
Variance30244265
MonotonicityNot monotonic
2023-11-10T14:32:41.339095image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1195.56 4
 
< 0.1%
996.3 4
 
< 0.1%
664.2 4
 
< 0.1%
6298.27 3
 
< 0.1%
1964.04 3
 
< 0.1%
7873.32 3
 
< 0.1%
3057.6 3
 
< 0.1%
1992.6 3
 
< 0.1%
2591.16 3
 
< 0.1%
1441.12 3
 
< 0.1%
Other values (9272) 9491
94.9%
(Missing) 476
 
4.8%
ValueCountFrequency (%)
34.14 1
< 0.1%
42.46 1
< 0.1%
46.62 1
< 0.1%
73.42 1
< 0.1%
73.94 1
< 0.1%
74.48 1
< 0.1%
79.68 1
< 0.1%
85.39 1
< 0.1%
94.81 1
< 0.1%
95.32 1
< 0.1%
ValueCountFrequency (%)
44231.08 1
< 0.1%
44121.01 1
< 0.1%
43391.6 1
< 0.1%
43004.93 1
< 0.1%
42651.38 1
< 0.1%
40986.76 1
< 0.1%
40929.44 1
< 0.1%
40889.22 1
< 0.1%
40153.71 1
< 0.1%
39981.69 1
< 0.1%

total_rec_prncp
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8644
Distinct (%)90.8%
Missing476
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean3808.5013
Minimum22.5
Maximum35000.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-11-10T14:32:41.579227image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum22.5
5-th percentile337.7345
Q11027.525
median2237.87
Q34544.47
95-th percentile13597.027
Maximum35000.01
Range34977.51
Interquartile range (IQR)3516.945

Descriptive statistics

Standard deviation4801.5012
Coefficient of variation (CV)1.2607325
Kurtosis11.95299
Mean3808.5013
Median Absolute Deviation (MAD)1435.875
Skewness3.0714179
Sum36272166
Variance23054414
MonotonicityNot monotonic
2023-11-10T14:32:41.874143image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12000 65
 
0.7%
10000 50
 
0.5%
6000 46
 
0.5%
20000 41
 
0.4%
15000 39
 
0.4%
16000 33
 
0.3%
5000 31
 
0.3%
8000 30
 
0.3%
18000 25
 
0.2%
35000 24
 
0.2%
Other values (8634) 9140
91.4%
(Missing) 476
 
4.8%
ValueCountFrequency (%)
22.5 1
< 0.1%
23.27 1
< 0.1%
26.75 1
< 0.1%
27.17 1
< 0.1%
41.18 1
< 0.1%
41.8 1
< 0.1%
45.87 1
< 0.1%
50.06 1
< 0.1%
50.39 1
< 0.1%
55.99 1
< 0.1%
ValueCountFrequency (%)
35000.01 1
 
< 0.1%
35000 24
0.2%
34975 1
 
< 0.1%
34677.5 1
 
< 0.1%
34350 1
 
< 0.1%
34000 1
 
< 0.1%
33950 3
 
< 0.1%
33600 1
 
< 0.1%
33425 1
 
< 0.1%
33075 1
 
< 0.1%

total_rec_int
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9229
Distinct (%)96.9%
Missing476
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean1412.894
Minimum11.64
Maximum13514.55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-11-10T14:32:42.126109image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum11.64
5-th percentile149.296
Q1468.1125
median947
Q31777.87
95-th percentile4406.5475
Maximum13514.55
Range13502.91
Interquartile range (IQR)1309.7575

Descriptive statistics

Standard deviation1489.2275
Coefficient of variation (CV)1.0540264
Kurtosis9.5744922
Mean1412.894
Median Absolute Deviation (MAD)574.47
Skewness2.5963065
Sum13456402
Variance2217798.6
MonotonicityNot monotonic
2023-11-10T14:32:42.385888image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
292.77 4
 
< 0.1%
351.32 4
 
< 0.1%
400.79 4
 
< 0.1%
231.13 3
 
< 0.1%
134.13 3
 
< 0.1%
986.41 3
 
< 0.1%
1453.8 3
 
< 0.1%
475.65 3
 
< 0.1%
4191.21 3
 
< 0.1%
195.36 3
 
< 0.1%
Other values (9219) 9491
94.9%
(Missing) 476
 
4.8%
ValueCountFrequency (%)
11.64 1
< 0.1%
12.45 1
< 0.1%
13.04 1
< 0.1%
13.4 1
< 0.1%
15.29 1
< 0.1%
15.33 1
< 0.1%
17.62 1
< 0.1%
18.7 1
< 0.1%
19.36 1
< 0.1%
19.71 1
< 0.1%
ValueCountFrequency (%)
13514.55 1
< 0.1%
13331.14 1
< 0.1%
13298.51 1
< 0.1%
12643.97 1
< 0.1%
12617.02 1
< 0.1%
12156.87 1
< 0.1%
11918.17 1
< 0.1%
11687.98 1
< 0.1%
11549.15 1
< 0.1%
11506.46 1
< 0.1%

wtd_loans
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

interest_rate
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

int_rate2
Text

MISSING 

Distinct134
Distinct (%)1.4%
Missing476
Missing (%)4.8%
Memory size78.2 KiB
2023-11-10T14:32:42.773196image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.8189836
Min length5

Characters and Unicode

Total characters55420
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.1%

Sample

1st row10.16%
2nd row8.90%
3rd row7.90%
4th row13.67%
5th row15.80%
ValueCountFrequency (%)
12.12 485
 
5.1%
13.11 432
 
4.5%
8.90 357
 
3.7%
14.33 351
 
3.7%
7.90 321
 
3.4%
11.14 318
 
3.3%
15.31 285
 
3.0%
16.29 265
 
2.8%
7.62 262
 
2.8%
10.16 223
 
2.3%
Other values (124) 6225
65.4%
2023-11-10T14:32:43.408355image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 10517
19.0%
. 9524
17.2%
% 9524
17.2%
2 4315
7.8%
9 3785
 
6.8%
0 3313
 
6.0%
7 2926
 
5.3%
3 2654
 
4.8%
6 2557
 
4.6%
5 2514
 
4.5%
Other values (2) 3791
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 36372
65.6%
Other Punctuation 19048
34.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 10517
28.9%
2 4315
11.9%
9 3785
 
10.4%
0 3313
 
9.1%
7 2926
 
8.0%
3 2654
 
7.3%
6 2557
 
7.0%
5 2514
 
6.9%
8 1910
 
5.3%
4 1881
 
5.2%
Other Punctuation
ValueCountFrequency (%)
. 9524
50.0%
% 9524
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 55420
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 10517
19.0%
. 9524
17.2%
% 9524
17.2%
2 4315
7.8%
9 3785
 
6.8%
0 3313
 
6.0%
7 2926
 
5.3%
3 2654
 
4.8%
6 2557
 
4.6%
5 2514
 
4.5%
Other values (2) 3791
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 55420
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 10517
19.0%
. 9524
17.2%
% 9524
17.2%
2 4315
7.8%
9 3785
 
6.8%
0 3313
 
6.0%
7 2926
 
5.3%
3 2654
 
4.8%
6 2557
 
4.6%
5 2514
 
4.5%
Other values (2) 3791
 
6.8%

num_rate
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

numrate
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

int_rate3
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct134
Distinct (%)1.4%
Missing476
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean14.277852
Minimum6.03
Maximum26.06
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-11-10T14:32:43.706957image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum6.03
5-th percentile7.62
Q111.14
median14.09
Q317.27
95-th percentile22.4
Maximum26.06
Range20.03
Interquartile range (IQR)6.13

Descriptive statistics

Standard deviation4.4301591
Coefficient of variation (CV)0.31028191
Kurtosis-0.46512934
Mean14.277852
Median Absolute Deviation (MAD)3.1
Skewness0.24772703
Sum135982.26
Variance19.62631
MonotonicityNot monotonic
2023-11-10T14:32:44.038166image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.12 485
 
4.9%
13.11 432
 
4.3%
8.9 357
 
3.6%
14.33 351
 
3.5%
7.9 321
 
3.2%
11.14 318
 
3.2%
15.31 285
 
2.9%
16.29 265
 
2.6%
7.62 262
 
2.6%
10.16 223
 
2.2%
Other values (124) 6225
62.3%
(Missing) 476
 
4.8%
ValueCountFrequency (%)
6.03 220
2.2%
6.62 184
1.8%
6.97 15
 
0.1%
7.51 12
 
0.1%
7.62 262
2.6%
7.9 321
3.2%
8.6 23
 
0.2%
8.9 357
3.6%
9.25 26
 
0.3%
9.67 75
 
0.8%
ValueCountFrequency (%)
26.06 6
 
0.1%
25.99 3
 
< 0.1%
25.89 8
0.1%
25.83 9
0.1%
25.8 9
0.1%
25.57 8
0.1%
25.28 5
 
0.1%
24.99 11
0.1%
24.89 15
0.1%
24.83 7
0.1%

Interactions

2023-11-10T14:32:25.579308image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:31:38.855129image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:31:42.028511image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:31:45.227183image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:31:48.360503image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:31:51.270320image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:31:54.395937image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:31:57.233466image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:32:00.447289image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:32:03.351897image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:32:05.833913image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:32:08.230101image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:32:11.097591image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:32:14.191977image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:32:16.595822image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
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2023-11-10T14:32:13.223683image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:32:15.902764image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:32:18.648360image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:32:21.458466image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:32:24.338020image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:32:28.776847image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:31:41.185502image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:31:44.576669image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:31:47.511531image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:31:50.726529image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:31:53.772637image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:31:56.725873image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:31:59.614809image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:32:02.814198image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:32:05.288475image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:32:07.689480image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:32:10.454717image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:32:13.423427image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:32:16.028520image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:32:18.820808image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:32:21.603589image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:32:24.565104image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:32:28.993481image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:31:41.347151image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:31:44.715929image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:31:47.705335image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:31:50.864517image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:31:53.934284image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:31:56.843158image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:31:59.799559image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:32:02.947916image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:32:05.415027image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:32:07.815258image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:32:10.605383image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:32:13.592025image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:32:16.142790image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:32:19.021141image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:32:21.721851image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:32:24.773595image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:32:29.226283image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:31:41.542776image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:31:44.870325image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:31:47.923371image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:31:50.999012image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:31:54.092812image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:31:56.973656image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:32:00.021894image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:32:03.081316image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:32:05.548586image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:32:07.951272image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:32:10.769828image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:32:13.777772image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:32:16.279037image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:32:19.182665image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:32:21.857945image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:32:25.019211image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:32:29.468127image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:31:41.774674image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:31:45.072239image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:31:48.142382image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:31:51.136251image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:31:54.244791image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:31:57.102488image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:32:00.240022image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:32:03.216091image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:32:05.689140image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:32:08.091023image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:32:10.934196image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:32:13.982058image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:32:16.435755image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:32:19.347300image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:32:21.994635image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T14:32:25.290968image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-11-10T14:32:44.384471image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
idloan_amntfunded_amntint_rateinstallmentannual_incdtidelinq_2yrsmths_since_last_delinqopen_accrevol_baltotal_accout_prncptotal_pymnttotal_rec_prncptotal_rec_intint_rate3termemp_lengthhome_ownershiploan_statuspurposeaddr_state
id1.0000.0000.0010.0750.0220.0390.0270.067-0.0480.0520.0050.0700.364-0.689-0.708-0.5040.0750.0960.0250.0400.1250.0510.033
loan_amnt0.0001.0001.0000.1430.9700.4800.0480.029-0.0530.2060.5000.2580.7510.5210.3960.6740.1430.4590.0430.1010.0210.1030.024
funded_amnt0.0011.0001.0000.1430.9700.4800.0480.028-0.0530.2060.5000.2580.7520.5210.3960.6740.1430.4580.0430.1010.0210.1030.024
int_rate0.0750.1430.1431.0000.139-0.0300.1420.115-0.082-0.0010.011-0.0250.1440.027-0.1500.3641.0000.4930.0000.0580.0430.0890.028
installment0.0220.9700.9700.1391.0000.4720.0480.038-0.0570.2040.4920.2420.7070.5490.4470.6670.1390.2980.0420.0860.0190.0980.026
annual_inc0.0390.4800.480-0.0300.4721.000-0.2270.099-0.0890.2320.4080.3420.3610.2510.2120.263-0.0300.0320.0050.0650.0000.0450.012
dti0.0270.0480.0480.1420.048-0.2271.000-0.0230.0530.2970.2340.2290.082-0.010-0.0420.0780.1420.0760.0190.0370.0340.0610.045
delinq_2yrs0.0670.0290.0280.1150.0380.099-0.0231.000-0.8230.060-0.0410.1650.031-0.020-0.0350.0120.1150.0050.0070.0000.0140.0130.037
mths_since_last_delinq-0.048-0.053-0.053-0.082-0.057-0.0890.053-0.8231.000-0.051-0.013-0.093-0.041-0.0050.009-0.015-0.0820.0360.0220.0300.0000.0000.000
open_acc0.0520.2060.206-0.0010.2040.2320.2970.060-0.0511.0000.3480.6630.1840.0800.0610.110-0.0010.0730.0120.0820.0080.0270.011
revol_bal0.0050.5000.5000.0110.4920.4080.234-0.041-0.0130.3481.0000.3130.3890.2770.2300.3270.0110.0410.0240.0660.0000.0270.033
total_acc0.0700.2580.258-0.0250.2420.3420.2290.165-0.0930.6630.3131.0000.2010.1010.0770.112-0.0250.1240.0490.1270.0060.0320.047
out_prncp0.3640.7510.7520.1440.7070.3610.0820.031-0.0410.1840.3890.2011.0000.041-0.0820.4280.1440.5060.0440.0990.2980.0760.032
total_pymnt-0.6890.5210.5210.0270.5490.251-0.010-0.020-0.0050.0800.2770.1010.0411.0000.9680.7680.0270.1100.0070.0210.2330.0440.025
total_rec_prncp-0.7080.3960.396-0.1500.4470.212-0.042-0.0350.0090.0610.2300.077-0.0820.9681.0000.627-0.1500.1680.0040.0170.2770.0360.012
total_rec_int-0.5040.6740.6740.3640.6670.2630.0780.012-0.0150.1100.3270.1120.4280.7680.6271.0000.3640.4150.0130.0290.0420.0360.029
int_rate30.0750.1430.1431.0000.139-0.0300.1420.115-0.082-0.0010.011-0.0250.1440.027-0.1500.3641.0000.4930.0000.0580.0430.0890.028
term0.0960.4590.4580.4930.2980.0320.0760.0050.0360.0730.0410.1240.5060.1100.1680.4150.4931.0000.0860.1210.0650.0900.058
emp_length0.0250.0430.0430.0000.0420.0050.0190.0070.0220.0120.0240.0490.0440.0070.0040.0130.0000.0861.0000.1070.0000.0180.016
home_ownership0.0400.1010.1010.0580.0860.0650.0370.0000.0300.0820.0660.1270.0990.0210.0170.0290.0580.1210.1071.0000.0000.0870.135
loan_status0.1250.0210.0210.0430.0190.0000.0340.0140.0000.0080.0000.0060.2980.2330.2770.0420.0430.0650.0000.0001.0000.0320.023
purpose0.0510.1030.1030.0890.0980.0450.0610.0130.0000.0270.0270.0320.0760.0440.0360.0360.0890.0900.0180.0870.0321.0000.027
addr_state0.0330.0240.0240.0280.0260.0120.0450.0370.0000.0110.0330.0470.0320.0250.0120.0290.0280.0580.0160.1350.0230.0271.000

Missing values

2023-11-10T14:32:29.864417image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-10T14:32:30.477740image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-11-10T14:32:30.978698image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idloan_amntfunded_amnttermint_rateinstallmentemp_lengthhome_ownershipannual_incloan_statuspurposeaddr_statedtidelinq_2yrsearliest_cr_linemths_since_last_delinqopen_accrevol_baltotal_accout_prncptotal_pymnttotal_rec_prncptotal_rec_intwtd_loansinterest_rateint_rate2num_ratenumrateint_rate3
0571203180001800060 months10.16383.8710+ yearsMORTGAGE72804.0Currentcredit_cardMA16.730.01995-12-27 02:06:00NaN21.08751.049.013263.187273.774736.822536.95NaNNaN10.16%NaNNaN10.16
1694891156751567536 months8.90497.7410+ yearsMORTGAGE100000.0Currentsmall_businessWA9.100.01994-04-07 12:00:00NaN16.020650.045.015294.25496.78380.75116.03NaNNaN8.90%NaNNaN8.90
2784712165001650060 months7.90333.782 yearsMORTGAGE42000.0Late (31-120 days)small_businessNY10.430.01993-07-16 08:41:00NaN9.02229.017.012966.645000.853533.361467.49NaNNaN7.90%NaNNaN7.90
38434485500550036 months13.67187.103 yearsRENT55000.0Fully Paiddebt_consolidationNJ20.710.01987-07-24 12:40:00NaN17.09486.025.00.005792.145500.00292.14NaNNaN13.67%NaNNaN13.67
49746546400640036 months15.80224.382 yearsRENT34000.0Currentdebt_consolidationVA32.400.01998-03-15 06:57:0047.06.04915.015.04430.592912.261969.41942.85NaNNaN15.80%NaNNaN15.80
510231191400140036 months15.9649.203 yearsMORTGAGE67000.0Fully Paidhome_improvementNV19.570.02004-01-01 12:16:0061.08.013806.014.00.001687.481400.00287.48NaNNaN15.96%NaNNaN15.96
610428716250625036 months7.51194.457 yearsMORTGAGE33600.0Currentdebt_consolidationCA18.050.02005-08-13 06:31:00NaN7.05174.010.02072.554847.254177.45669.80NaNNaN7.51%NaNNaN7.51
710551937300730036 months13.49247.703 yearsRENT50000.0Currentsmall_businessFL19.060.02006-01-03 09:58:00NaN8.012026.013.02554.606185.504745.401440.10NaNNaN13.49%NaNNaN13.49
81059509200002000060 months17.27499.9610+ yearsMORTGAGE80000.0Currentdebt_consolidationVA15.060.02001-03-04 05:02:00NaN11.021592.030.013404.0412928.216595.966332.25NaNNaN17.27%NaNNaN17.27
91063649175001680060 months22.74471.106 yearsMORTGAGE95000.0Charged Offdebt_consolidationWA24.780.02002-01-30 07:50:00NaN12.023722.023.00.004704.901662.303042.60NaNNaN22.74%NaNNaN22.74
idloan_amntfunded_amnttermint_rateinstallmentemp_lengthhome_ownershipannual_incloan_statuspurposeaddr_statedtidelinq_2yrsearliest_cr_linemths_since_last_delinqopen_accrevol_baltotal_accout_prncptotal_pymnttotal_rec_prncptotal_rec_intwtd_loansinterest_rateint_rate2num_ratenumrateint_rate3
99901007589820002000NaNNaNNaNNaNNaNNaNNaNNaNNJNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
99911009047540004000NaNNaNNaNNaNNaNNaNNaNNaNVANaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
99921009211980008000NaNNaNNaNNaNNaNNaNNaNNaNGANaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
9993100928611062510625NaNNaNNaNNaNNaNNaNNaNNaNTNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
99941010519765006500NaNNaNNaNNaNNaNNaNNaNNaNMONaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
9995101057781000010000NaNNaNNaNNaNNaNNaNNaNNaNKYNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
9996101099491500015000NaNNaNNaNNaNNaNNaNNaNNaNCANaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
99971011218735003500NaNNaNNaNNaNNaNNaNNaNNaNNYNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
9998101198971000010000NaNNaNNaNNaNNaNNaNNaNNaNCANaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
99991012310040004000NaNNaNNaNNaNNaNNaNNaNNaNGANaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN